Summary of Exploring Action-centric Representations Through the Lens Of Rate-distortion Theory, by Miguel De Llanza Varona et al.
Exploring Action-Centric Representations Through the Lens of Rate-Distortion Theory
by Miguel de Llanza Varona, Christopher L. Buckley, Beren Millidge
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Neurons and Cognition (q-bio.NC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The efficient coding hypothesis posits that organisms strive to maximize information about sensory inputs while minimizing resources in high-dimensional environments. Bayesian inference highlights the brain’s role in efficiently allocating resources for predicting hidden states causing sensory data. However, this framework neglects the action-oriented aspect of perception. Rate-distortion theory, which formalizes optimal lossy compression under constraints, is explored as a framework for goal-oriented efficient coding. In this work, we investigate action-centric representations within rate-distortion theory, defining abstractions mathematically and arguing they can be used to fix action-centric representation content. We model these representations using VAEs, finding that they i) efficiently compress data; ii) capture task-dependent invariances for successful behavior; and iii) are not aimed at reconstructing the data. This teleological approach to perception aligns with the conclusion that full reconstruction of data is rarely necessary for optimal behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores how living things process information from their environment. It’s like trying to figure out what’s important and what’s not in a big library. The authors want to understand how animals and humans can do this efficiently, using the right amount of energy and resources. They use math and computer models to show that when we’re doing something specific, like finding food or avoiding danger, our brains don’t need to remember every single detail. Instead, they just focus on what’s important for that task. This is a new way of thinking about how our brains work and how we can make decisions. |
Keywords
» Artificial intelligence » Bayesian inference
